1 Validation of an improved location-based handover algorithm using GSM measurement data Hsin-Piao Lin; Rong-Terng Juang; Ding-Bing Lin IEEE Transactions on Mobile Computing, Vol. 4, No 5, Sep. 2005

2 Outline Introduction Proposed Handover Algorithm Analysis of Handover Performance with Location Errors Verifying Performance Using GSM Measurement Data Conclusion

Introduction HANDOVER –the mechanism by which an ongoing call is transferred from one base station (BS) to another. Frequent handovers influence the QoS, increase the signaling overhead on the network, and degrade throughput in data communications. 3

Introduction (cont’d) Metrics used to support handover decision –received signal strength (RSS), –signal to interference ratio (SIR), –distance between the mobile and BS, –traffic load, and –mobile velocity RSS  mostly commonly used –constant handover threshold value (handover margin) too small  unnecessary handovers Too large  the QoS could be low and calls could be dropped –ping-pong effect Caused by the fluctuations of signal strength associated with shadow fadings 4

Introduction (cont’d) Most handover algorithms that are based on information about mobile location, suffer from a lack of practicability. The computational complexity of making a handover decision using fuzzy logic is excessive, Establishing and updating a lookup table to support a handover margin decision is time-consuming Selecting a handover algorithm based on the handover scenario –only succeeds in the preclassified environments, and –involves complicated processes to define the handover scenarios. –relies on an updated database Assuming GPS capable mobile telephone 5

Introduction (cont’d) The proposed handover algorithm –based on the estimates of mobile location (not using GPS) and velocity in a lognormal fading environment. identify the correlation among shadowing effects –was applied to a living GSM system in urban Taipei city. –Low computational complexity –does not employ a database or lookup table signal level = path loss + shadow fading –The variation in the signal caused by shadow fading depends on the location and velocity of the mobile station 6

System Model the signal power levels received from BS x at time index k: P x [k] = m x [k]+u x [k] –m x is the received signal powers from BS x in terms only of path loss, –u x is the respective shadow fadings 7

System Model (cont’d) The autocorrelation coefficient of the shadow fadings is commonly assumed to be an exponential function [11][12] –σ i is the standard deviation of shadow fadings; – △ d = V ． | k 2 - k 1 | ． τ V is mobile velocity, τ = 480 ms – is the decay distance (or correlation distance) 8 [11] M. Gudmundson, “Correlation Model for Shadow Fading in Mobile Radio Systems,” Electronics Letters, vol. 27, no. 23, pp , Nov [12] D. Giancristofaro, “Correlation Model for Shadow Fading in Mobile Radio Channels,” Electronics Letters, vol. 32, pp , May

System Model (cont’d) The cross-correlation coefficient of shadow fadings –The correlation depends on the angle between the two paths along the mobile to BS 1 and BS 2, and the relative values of the two path lengths. 9

Proposed Handover Algorithm The difference between signal powers received from BS 2 and BS 1 at time index k: A handover from BS 1 to BS 2 occurs at time index k if –Because of shadowing, unnecessary handovers may be performed if a handover decision is based only on Criterion 1. –Criterion 2 is imposed to improve the handover performance by determining whether path loss dominates the variation in the received signal strength. 10

Proposed Handover Algorithm (cont’d) Assume u 21 [k] and u 21 [k-ξ] are highly correlated, such that the correlation coefficient approaches unity The difference between P 21 [k] and P 21 [k-ξ] the difference between signal powers is always chiefly a function of path loss but not of shadow fadings the proposed algorithm ensures that the signal power received from the target BS is h dB higher than that received from the serving BS (criterion 1), and that the difference between the signal powers is dominated by path losses associated with motion of the mobile station (criterion 2). Hence, unnecessary handovers caused by fluctuations in shadow fadings can be avoided. 11

Proposed Handover Algorithm (cont’d) ξ is critical to handover performance –guarantee high correlation between u 21 [k] and u 21 [k-ξ], and sufficient space for signal variation caused by path loss –too large  Criterion 2 is always met –too small  the signal dose not vary 12

Proposed Handover Algorithm (cont’d) the standard deviations of shadow fadings are assumed to be equal, such that σ 1 = σ 2 = σ u Given u 1 [k], then based on the Gauss-Markov process –where X 1, X 2, and X 3 are identical independent Gaussian processes with zero-mean and variance σ u 2 and 13

Proposed Handover Algorithm (cont’d) Assume ρ 12 = ρ 21 = ρ c and ρ 11 = ρ 22 = ρ a, then The correlation between u 21 [k] and u 21 [k-ξ] is The correlation coefficient between u 21 [k] and u 21 [k-ξ] must exceed a threshold ρ T, then 14

15 location estimation using [15] [15] D.B. Lin, R.T. Juang, H.P. Lin, and C.Y. Ke, “Mobile Location Estimation Based on Differences of Signal Attenuations for GSM Systems,” Proc. IEEE Soc. Int’l Conf. Antennas and Propagation, vol.1, pp , June 2003.

Simulation using SignalPro by EDX Engineering –includes a set of planning tools for wireless communication system KM 1.6 KM omnidirectional antenna The height of each BS is 35 m the mean and standard deviation of their transmitting power (EIRP) are 42.6 dBm and 3.5 dB. The Walfisch-Ikegami model was applied to simulate the path loss. = 65 m, ρ c =0.1 V = 30 km/h, ρ T =0.85 handover alarm threshold = -80 dBm handover margin = 6dB

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Analysis of Handover Performance with Location Errors The velocity of the mobile station was estimated based on Doppler frequency shift in [18]. –However, the estimated Doppler frequency is unreachable in most standards of mobile cellular systems. This paper presents a means of estimating mobile velocity based on mobile location estimations. 18 [18] G. Azemi, B. Senabji, and B. Boashash, “A Novel Estimator for the Velocity of a Mobile Station in a Micro-Cellular System,” Proc. Int’l Symp. Circuits and Systems, vol. 2, pp , May 2003.

For one-dimensional case, the estimated location –L[k]: the actual mobile location –n L : the location error, which is modeled as a zero-mean Gaussian process with variance σ L For two-dimensional case 19

The accuracy of the estimate of ξ is very high because –It is run off during handover decision –It is a positive nonzero integer, which resulting in ξ=1 with very high probability 20

Using the proposed algorithm reduces the number of handovers (9-17%) and only slightly increases in signal outage probability. 21

Verifying Performance using GSM Measurement Data The proposed handover algorithm was applied to a living GSM system (1,800MHz) in urban Taipei city KM 2.1 KM averaged cell radius of around 330 m. The mean and standard deviation of building heights are 20.3 m and 14.4 m. The average and standard deviation of BS heights are 26.4 m and 10.2 m.

Verifying Performance using GSM Measurement Data (cont’d) Investigate the propagation characteristics (shadowing components) Estimate the cross-correlation coefficient of shadowing fadings. Estimate the correlation distance of shadowing fadings. The measurements data were applied for simulations of the proposed handover algorithm. 23

Verifying Performance using GSM Measurement Data (cont’d) 24 [15] D.B. Lin, R.T. Juang, H.P. Lin, and C.Y. Ke, “Mobile Location Estimation Based on Differences of Signal Attenuations for GSM Systems,” Proc. IEEE Soc. Int’l Conf. Antennas and Propagation, vol.1, pp , June 2003.

Verifying Performance using GSM Measurement Data (cont’d) the proposed handover algorithm reduces the number of handovers (18-26%) and only slightly increases the signal outage probability 25

Conclusion An improved handover algorithm for suppressing the ping-pong effect in cellular systems is verified by the GSM measurement data. –estimating the velocity of the mobile station based on non- GPS location techniques –Low computational complexity, and –no database or lookup table is required. The simulations indicate that the number of unnecessary handovers can be reduced 18-26%, while the signal outage probability remains similar. 26